{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detection of Twitter users who use hateful lexicon using graph machine learning with Stellargraph\n",
    "\n",
    "We consider the use-case of identifying hateful users on Twitter motivated by the work in [1] and using the dataset also published in [1]. Classification is based on a graph based on users' retweets and attributes as related to their account activity, and the content of tweets.\n",
    "\n",
    "We pose identifying hateful users as a binary classification problem. We demonstrate the advantage of connected vs unconnected data in a semi-supervised setting with few training examples.\n",
    "\n",
    "For connected data, we use Graph Neural Network methods, GCN [2], GAT [3], and GraphSAGE [4] as implemented in the `stellargraph` library. We pose the problem of identifying hateful tweeter users as node attribute inference in graphs.\n",
    "\n",
    "**References**\n",
    "\n",
    "1. \"Like Sheep Among Wolves\": Characterizing Hateful Users on Twitter. M. H. Ribeiro, P. H. Calais, Y. A. Santos, V. A. F. Almeida, and W. Meira Jr.  arXiv preprint arXiv:1801.00317 (2017).\n",
    "\n",
    "\n",
    "2. Semi-Supervised Classification with Graph Convolutional Networks. T. Kipf, M. Welling. ICLR 2017. arXiv:1609.02907 \n",
    "\n",
    "\n",
    "3. Graph Attention Networks. P. Velickovic et al. ICLR 2018\n",
    "\n",
    "\n",
    "4. Inductive Representation Learning on Large Graphs. W.L. Hamilton, R. Ying, and J. Leskovec arXiv:1706.02216 \n",
    "[cs.SI], 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "import os\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "import stellargraph as sg\n",
    "from stellargraph.mapper import GraphSAGENodeGenerator, FullBatchNodeGenerator\n",
    "from stellargraph.layer import GraphSAGE, GCN, GAT\n",
    "from stellargraph import globalvar\n",
    "\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model, models\n",
    "from sklearn import preprocessing, feature_extraction\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def remove_prefix(text, prefix):\n",
    "    return text[text.startswith(prefix) and len(prefix):]\n",
    "\n",
    "def plot_history(history):\n",
    "    metrics = sorted(set([remove_prefix(m, \"val_\") for m in list(history.history.keys())]))\n",
    "    for m in metrics:\n",
    "        # summarize history for metric m\n",
    "        plt.plot(history.history[m])\n",
    "        plt.plot(history.history['val_' + m])\n",
    "        plt.title(m, fontsize=18)\n",
    "        plt.ylabel(m, fontsize=18)\n",
    "        plt.xlabel('epoch', fontsize=18)\n",
    "        plt.legend(['train', 'validation'], loc='best')\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Downloading the dataset:**\n",
    "\n",
    "The dataset for this demo was published in [1] and it is freely available to download from Kaggle [here](https://www.kaggle.com/manoelribeiro/hateful-users-on-twitter/home).\n",
    "\n",
    "The following is the description of the datasets:\n",
    "\n",
    ">This dataset contains a network of 100k users, out of which ~5k were annotated as hateful or\n",
    ">not. For each user, several content-related, network-related and activity related features\n",
    ">were provided. \n",
    "\n",
    "Additional files of hateful lexicon can be found [here]( \n",
    "https://github.com/manoelhortaribeiro/HatefulUsersTwitter/tree/master/data/extra)\n",
    "\n",
    "Download the dataset and then set the `data_dir` variable to point to the download location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = os.path.expanduser(\"~/data/hateful-twitter-users\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First load and prepare the node features\n",
    "\n",
    "Each node in the graph is associated with a large number of features (also referred to as attributes). \n",
    "\n",
    "The list of features is given [here](https://www.kaggle.com/manoelribeiro/hateful-users-on-twitter). We repeated here for convenience.\n",
    "\n",
    "hate :(\"hateful\"|\"normal\"|\"other\")\n",
    "  if user was annotated as hateful, normal, or not annotated.\n",
    "  \n",
    "  (is_50|is_50_2) :bool\n",
    "  whether user was deleted up to 12/12/17 or 14/01/18. \n",
    "  \n",
    "  (is_63|is_63_2) :bool\n",
    "  whether user was suspended up to 12/12/17 or 14/01/18. \n",
    "        \n",
    "  (hate|normal)_neigh :bool\n",
    "  is the user on the neighborhood of a (hateful|normal) user? \n",
    "  \n",
    "  [c_] (statuses|follower|followees|favorites)_count :int\n",
    "  number of (tweets|follower|followees|favorites) a user has.\n",
    "  \n",
    "  [c_] listed_count:int\n",
    "  number of lists a user is in.\n",
    "\n",
    "  [c_] (betweenness|eigenvector|in_degree|outdegree) :float\n",
    "  centrality measurements for each user in the retweet graph.\n",
    "  \n",
    "  [c_] *_empath :float\n",
    "  occurrences of empath categories in the users latest 200 tweets.\n",
    "\n",
    "  [c_] *_glove :float          \n",
    "  glove vector calculated for users latest 200 tweets.\n",
    "  \n",
    "  [c_] (sentiment|subjectivity) :float\n",
    "  average sentiment and subjectivity of users tweets.\n",
    "  \n",
    "  [c_] (time_diff|time_diff_median) :float\n",
    "  average and median time difference between tweets.\n",
    "  \n",
    "  [c_] (tweet|retweet|quote) number :float\n",
    "  percentage of direct tweets, retweets and quotes of an user.\n",
    "  \n",
    "  [c_] (number urls|number hashtags|baddies|mentions) :float\n",
    "  number of bad words|mentions|urls|hashtags per tweet in average.\n",
    "  \n",
    "  [c_] status length :float\n",
    "  average status length.\n",
    "  \n",
    "  hashtags :string\n",
    "  all hashtags employed by the user separated by spaces.\n",
    "  \n",
    "**Notice** that c_ are attributes calculated for the 1-neighborhood of a user in the retweet network (averaged out)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we are going to load the user features and prepare them for machine learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>hate</th>\n",
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       "      <th>statuses_count</th>\n",
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       "      <th>listed_count</th>\n",
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       "      <th>...</th>\n",
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       "      <td>False</td>\n",
       "      <td>1044</td>\n",
       "      <td>2371</td>\n",
       "      <td>2246</td>\n",
       "      <td>561</td>\n",
       "      <td>16</td>\n",
       "      <td>4897.117853</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000985</td>\n",
       "      <td>0.005284</td>\n",
       "      <td>0.000554</td>\n",
       "      <td>0.001084</td>\n",
       "      <td>0.001359</td>\n",
       "      <td>0.002041</td>\n",
       "      <td>0.016846</td>\n",
       "      <td>0.004881</td>\n",
       "      <td>0.001214</td>\n",
       "      <td>0.003347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>167172</td>\n",
       "      <td>3004</td>\n",
       "      <td>298</td>\n",
       "      <td>3242</td>\n",
       "      <td>53</td>\n",
       "      <td>9.864754</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001391</td>\n",
       "      <td>0.002061</td>\n",
       "      <td>0.001116</td>\n",
       "      <td>0.001282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001951</td>\n",
       "      <td>0.015423</td>\n",
       "      <td>0.000446</td>\n",
       "      <td>0.000446</td>\n",
       "      <td>0.005241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>other</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1998</td>\n",
       "      <td>17643</td>\n",
       "      <td>19355</td>\n",
       "      <td>485</td>\n",
       "      <td>239</td>\n",
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       "      <td>0.035382</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>0.000475</td>\n",
       "      <td>0.000475</td>\n",
       "      <td>0.002431</td>\n",
       "      <td>0.007656</td>\n",
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       "      <td>0.072792</td>\n",
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       "<p>5 rows × 1039 columns</p>\n",
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      ],
      "text/plain": [
       "   user_id    hate  hate_neigh  normal_neigh  statuses_count  followers_count  \\\n",
       "0        0  normal        True          True          101767             3504   \n",
       "1        1   other       False         False            2352            19609   \n",
       "2        2   other       False         False            1044             2371   \n",
       "3        3   other       False         False          167172             3004   \n",
       "4        4   other       False         False            1998            17643   \n",
       "\n",
       "   followees_count  favorites_count  listed_count    betweenness  ...  \\\n",
       "0             3673            81635            53  100467.895084  ...   \n",
       "1              309               61           197       0.000000  ...   \n",
       "2             2246              561            16    4897.117853  ...   \n",
       "3              298             3242            53       9.864754  ...   \n",
       "4            19355              485           239       0.000000  ...   \n",
       "\n",
       "   c_feminine_empath  c_medieval_empath  c_journalism_empath  \\\n",
       "0           0.001380           0.003288             0.000255   \n",
       "1           0.000802           0.004465             0.000444   \n",
       "2           0.000985           0.005284             0.000554   \n",
       "3           0.001391           0.002061             0.001116   \n",
       "4           0.000633           0.035382             0.000317   \n",
       "\n",
       "   c_farming_empath  c_plant_empath  c_shopping_empath  c_ship_empath  \\\n",
       "0          0.002189        0.000593           0.003689       0.003559   \n",
       "1          0.001632        0.001298           0.002183       0.008969   \n",
       "2          0.001084        0.001359           0.002041       0.016846   \n",
       "3          0.001282        0.000000           0.001951       0.015423   \n",
       "4          0.000475        0.000475           0.002431       0.007656   \n",
       "\n",
       "   c_religion_empath  c_tourism_empath  c_power_empath  \n",
       "0           0.001634          0.002662        0.007487  \n",
       "1           0.004975          0.000647        0.003419  \n",
       "2           0.004881          0.001214        0.003347  \n",
       "3           0.000446          0.000446        0.005241  \n",
       "4           0.033273          0.072792        0.003698  \n",
       "\n",
       "[5 rows x 1039 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_feat = pd.read_csv(os.path.join(data_dir, \n",
    "                                      'users_neighborhood_anon.csv'))\n",
    "users_feat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the distribution of hateful, normal (not hateful), and other (unknown) users in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial hateful/normal users distribution\n",
      "(100386, 1039)\n",
      "other      95415\n",
      "normal      4427\n",
      "hateful      544\n",
      "Name: hate, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Initial hateful/normal users distribution\")\n",
    "print(users_feat.shape)\n",
    "print(users_feat.hate.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a clear imbalance on the number of users tagged as hateful vs normal and unknown."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data cleaning and preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset as given includes a large number of graph related features that are manually extracted. \n",
    "\n",
    "Since we are going to employ modern graph neural networks methods for classification, we are going to drop these manually engineered features. \n",
    "\n",
    "The power of Graph Neural Networks stems from their ability to learn useful graph-related features eliminating the need for manual feature engineering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_cleaning(feat):\n",
    "    feat = feat.drop(columns=[\"hate_neigh\", \"normal_neigh\"])\n",
    "    \n",
    "    # Convert target values in hate column from strings to integers (0,1,2)\n",
    "    feat['hate'] = np.where(feat['hate']=='hateful', 1, np.where(feat['hate']=='normal', 0, 2))\n",
    "    \n",
    "    # missing information\n",
    "    number_of_missing = feat.isnull().sum()\n",
    "    number_of_missing[number_of_missing!=0]\n",
    "    \n",
    "    # Replace NA with 0\n",
    "    feat.fillna(0, inplace=True)\n",
    "\n",
    "    # droping info about suspension and deletion as it is should not be use din the predictive model\n",
    "    feat.drop(feat.columns[feat.columns.str.contains(\"is_\")], axis=1, inplace=True)\n",
    "\n",
    "    # drop glove features\n",
    "    feat.drop(feat.columns[feat.columns.str.contains(\"_glove\")], axis=1, inplace=True)\n",
    "\n",
    "    # drop c_ features\n",
    "    feat.drop(feat.columns[feat.columns.str.contains(\"c_\")], axis=1, inplace=True)\n",
    "\n",
    "    # drop sentiment features for now\n",
    "    feat.drop(feat.columns[feat.columns.str.contains(\"sentiment\")], axis=1, inplace=True)\n",
    "\n",
    "    # drop hashtag feature\n",
    "    feat.drop(['hashtags'], axis=1, inplace=True)\n",
    "\n",
    "    # Drop centrality based measures\n",
    "    feat.drop(columns=['betweenness', 'eigenvector', 'in_degree', 'out_degree'], inplace=True)\n",
    "    \n",
    "    feat.drop(columns=['created_at'], inplace=True)\n",
    "    \n",
    "    return feat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_data = data_cleaning(users_feat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of the original **1037** node features, we are keeping only **204** that are based on a user's attributes and tweet lexicon. We have removed any manually engineered graph features since the graph neural network algorithms we are going to use will automatically determine the best features to use during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100386, 206)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>hate</th>\n",
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       "      <th>tweet number</th>\n",
       "      <th>retweet number</th>\n",
       "      <th>quote number</th>\n",
       "      <th>status length</th>\n",
       "      <th>number urls</th>\n",
       "      <th>baddies</th>\n",
       "      <th>mentions</th>\n",
       "      <th>time_diff</th>\n",
       "      <th>time_diff_median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>101767</td>\n",
       "      <td>3504</td>\n",
       "      <td>3673</td>\n",
       "      <td>81635</td>\n",
       "      <td>53</td>\n",
       "      <td>0.000513</td>\n",
       "      <td>0.002564</td>\n",
       "      <td>0.002564</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>75.565000</td>\n",
       "      <td>82.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>356.020101</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2352</td>\n",
       "      <td>19609</td>\n",
       "      <td>309</td>\n",
       "      <td>61</td>\n",
       "      <td>197</td>\n",
       "      <td>0.003180</td>\n",
       "      <td>0.000867</td>\n",
       "      <td>0.003469</td>\n",
       "      <td>...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.713568</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17519.116162</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1044</td>\n",
       "      <td>2371</td>\n",
       "      <td>2246</td>\n",
       "      <td>561</td>\n",
       "      <td>16</td>\n",
       "      <td>0.005272</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005272</td>\n",
       "      <td>...</td>\n",
       "      <td>328.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.130000</td>\n",
       "      <td>219.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>46417.758794</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>167172</td>\n",
       "      <td>3004</td>\n",
       "      <td>298</td>\n",
       "      <td>3242</td>\n",
       "      <td>53</td>\n",
       "      <td>0.004016</td>\n",
       "      <td>0.005801</td>\n",
       "      <td>0.001339</td>\n",
       "      <td>...</td>\n",
       "      <td>127.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>85.760000</td>\n",
       "      <td>149.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>656.889447</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1998</td>\n",
       "      <td>17643</td>\n",
       "      <td>19355</td>\n",
       "      <td>485</td>\n",
       "      <td>239</td>\n",
       "      <td>0.001134</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000756</td>\n",
       "      <td>...</td>\n",
       "      <td>1710.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>152.175000</td>\n",
       "      <td>198.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>55991.356784</td>\n",
       "      <td>48197.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 206 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  hate  statuses_count  followers_count  followees_count  \\\n",
       "0        0     0          101767             3504             3673   \n",
       "1        1     2            2352            19609              309   \n",
       "2        2     2            1044             2371             2246   \n",
       "3        3     2          167172             3004              298   \n",
       "4        4     2            1998            17643            19355   \n",
       "\n",
       "   favorites_count  listed_count  negotiate_empath  vehicle_empath  \\\n",
       "0            81635            53          0.000513        0.002564   \n",
       "1               61           197          0.003180        0.000867   \n",
       "2              561            16          0.005272        0.000000   \n",
       "3             3242            53          0.004016        0.005801   \n",
       "4              485           239          0.001134        0.000000   \n",
       "\n",
       "   science_empath  ...  number hashtags  tweet number  retweet number  \\\n",
       "0        0.002564  ...             16.0         121.0            79.0   \n",
       "1        0.003469  ...             40.0         199.0             0.0   \n",
       "2        0.005272  ...            328.0         113.0            87.0   \n",
       "3        0.001339  ...            127.0          69.0           131.0   \n",
       "4        0.000756  ...           1710.0         101.0            99.0   \n",
       "\n",
       "   quote number  status length  number urls  baddies  mentions     time_diff  \\\n",
       "0           5.0      75.565000         82.0     18.0     159.0    356.020101   \n",
       "1           0.0     101.713568         20.0     10.0       6.0  17519.116162   \n",
       "2           0.0     128.130000        219.0     16.0     158.0  46417.758794   \n",
       "3           3.0      85.760000        149.0     10.0      26.0    656.889447   \n",
       "4           0.0     152.175000        198.0     35.0       7.0  55991.356784   \n",
       "\n",
       "   time_diff_median  \n",
       "0              74.0  \n",
       "1              45.0  \n",
       "2            2010.0  \n",
       "3              72.0  \n",
       "4           48197.0  \n",
       "\n",
       "[5 rows x 206 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The continous features in our dataset have distributions with very long tails. We apply normalization to correct for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ignore the first two columns because those are user_id and hate (the target variable)\n",
    "df_values = node_data.iloc[:, 2:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pt = preprocessing.PowerTransformer(method='yeo-johnson', \n",
    "                                    standardize=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_values_log = pt.fit_transform(df_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at one of the normalized features before and after the power transform was applied.\n",
    "\n",
    "The feature we are going to look at is a user's number of followers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns_rc = {'lines.linewidth': 3, 'figure.figsize':(12,6)}\n",
    "sns.set_context(\"paper\", rc = sns_rc) \n",
    "sns.set_style(\"whitegrid\", {'axes.grid' : False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(df_values[:, 1])\n",
    "s = plt.ylabel(\"Density\", fontsize=18)\n",
    "s = plt.xlabel(\"Feature value\", fontsize=18)\n",
    "s = plt.title(\"Number of followers, before Power Transform\", fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(df_values_log[:, 1])\n",
    "s = plt.ylabel(\"Density\", fontsize=18)\n",
    "s = plt.xlabel(\"Feature value\", fontsize=18)\n",
    "s = plt.title(\"Number of followers after Power Transform\", fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature normalization looks like it is doing the right thing as the raw features have long tails that are eliminated after applying the power transform. \n",
    "\n",
    "So let us use the normalized features from now on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_data.iloc[:, 2:] = df_values_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the dataframe index to be the same as the user_id and drop the user_id columns\n",
    "node_data.index = node_data.index.map(str)\n",
    "node_data.drop(columns=['user_id'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Node features are now ready for machine learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hate</th>\n",
       "      <th>statuses_count</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>followees_count</th>\n",
       "      <th>favorites_count</th>\n",
       "      <th>listed_count</th>\n",
       "      <th>negotiate_empath</th>\n",
       "      <th>vehicle_empath</th>\n",
       "      <th>science_empath</th>\n",
       "      <th>timidity_empath</th>\n",
       "      <th>...</th>\n",
       "      <th>number hashtags</th>\n",
       "      <th>tweet number</th>\n",
       "      <th>retweet number</th>\n",
       "      <th>quote number</th>\n",
       "      <th>status length</th>\n",
       "      <th>number urls</th>\n",
       "      <th>baddies</th>\n",
       "      <th>mentions</th>\n",
       "      <th>time_diff</th>\n",
       "      <th>time_diff_median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.541150</td>\n",
       "      <td>0.046773</td>\n",
       "      <td>1.104767</td>\n",
       "      <td>1.869391</td>\n",
       "      <td>0.017835</td>\n",
       "      <td>-1.752256</td>\n",
       "      <td>0.164900</td>\n",
       "      <td>0.181173</td>\n",
       "      <td>0.875069</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.632488</td>\n",
       "      <td>-0.049013</td>\n",
       "      <td>0.321929</td>\n",
       "      <td>-0.369992</td>\n",
       "      <td>-1.036127</td>\n",
       "      <td>-0.796091</td>\n",
       "      <td>0.047430</td>\n",
       "      <td>0.356495</td>\n",
       "      <td>-1.888186</td>\n",
       "      <td>-1.299249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>-0.700240</td>\n",
       "      <td>0.772450</td>\n",
       "      <td>-0.526061</td>\n",
       "      <td>-1.434183</td>\n",
       "      <td>0.613187</td>\n",
       "      <td>-0.735320</td>\n",
       "      <td>-0.864337</td>\n",
       "      <td>0.599279</td>\n",
       "      <td>1.610977</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.071721</td>\n",
       "      <td>1.479066</td>\n",
       "      <td>-1.999580</td>\n",
       "      <td>-1.545285</td>\n",
       "      <td>-0.188945</td>\n",
       "      <td>-1.875745</td>\n",
       "      <td>-0.626192</td>\n",
       "      <td>-1.972207</td>\n",
       "      <td>0.160925</td>\n",
       "      <td>-1.512603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-1.077284</td>\n",
       "      <td>-0.127775</td>\n",
       "      <td>0.767345</td>\n",
       "      <td>-0.669050</td>\n",
       "      <td>-0.523882</td>\n",
       "      <td>-0.118440</td>\n",
       "      <td>-1.573040</td>\n",
       "      <td>1.211083</td>\n",
       "      <td>-0.154213</td>\n",
       "      <td>...</td>\n",
       "      <td>1.618609</td>\n",
       "      <td>-0.201320</td>\n",
       "      <td>0.452537</td>\n",
       "      <td>-1.545285</td>\n",
       "      <td>0.637869</td>\n",
       "      <td>0.884530</td>\n",
       "      <td>-0.096918</td>\n",
       "      <td>0.348954</td>\n",
       "      <td>0.698841</td>\n",
       "      <td>0.122176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1.908494</td>\n",
       "      <td>-0.021575</td>\n",
       "      <td>-0.548705</td>\n",
       "      <td>0.078540</td>\n",
       "      <td>0.017835</td>\n",
       "      <td>-0.472125</td>\n",
       "      <td>1.281633</td>\n",
       "      <td>-0.544862</td>\n",
       "      <td>1.259492</td>\n",
       "      <td>...</td>\n",
       "      <td>0.781915</td>\n",
       "      <td>-1.018822</td>\n",
       "      <td>1.085858</td>\n",
       "      <td>-0.662393</td>\n",
       "      <td>-0.701835</td>\n",
       "      <td>0.088472</td>\n",
       "      <td>-0.626192</td>\n",
       "      <td>-1.254997</td>\n",
       "      <td>-1.576801</td>\n",
       "      <td>-1.311031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>-0.778589</td>\n",
       "      <td>0.729918</td>\n",
       "      <td>2.296049</td>\n",
       "      <td>-0.725089</td>\n",
       "      <td>0.700128</td>\n",
       "      <td>-1.488804</td>\n",
       "      <td>-1.573040</td>\n",
       "      <td>-0.969812</td>\n",
       "      <td>0.199834</td>\n",
       "      <td>...</td>\n",
       "      <td>3.422701</td>\n",
       "      <td>-0.427866</td>\n",
       "      <td>0.638106</td>\n",
       "      <td>-1.545285</td>\n",
       "      <td>1.370832</td>\n",
       "      <td>0.655433</td>\n",
       "      <td>0.955922</td>\n",
       "      <td>-1.914894</td>\n",
       "      <td>0.803553</td>\n",
       "      <td>1.472247</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 205 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   hate  statuses_count  followers_count  followees_count  favorites_count  \\\n",
       "0     0        1.541150         0.046773         1.104767         1.869391   \n",
       "1     2       -0.700240         0.772450        -0.526061        -1.434183   \n",
       "2     2       -1.077284        -0.127775         0.767345        -0.669050   \n",
       "3     2        1.908494        -0.021575        -0.548705         0.078540   \n",
       "4     2       -0.778589         0.729918         2.296049        -0.725089   \n",
       "\n",
       "   listed_count  negotiate_empath  vehicle_empath  science_empath  \\\n",
       "0      0.017835         -1.752256        0.164900        0.181173   \n",
       "1      0.613187         -0.735320       -0.864337        0.599279   \n",
       "2     -0.523882         -0.118440       -1.573040        1.211083   \n",
       "3      0.017835         -0.472125        1.281633       -0.544862   \n",
       "4      0.700128         -1.488804       -1.573040       -0.969812   \n",
       "\n",
       "   timidity_empath  ...  number hashtags  tweet number  retweet number  \\\n",
       "0         0.875069  ...        -0.632488     -0.049013        0.321929   \n",
       "1         1.610977  ...        -0.071721      1.479066       -1.999580   \n",
       "2        -0.154213  ...         1.618609     -0.201320        0.452537   \n",
       "3         1.259492  ...         0.781915     -1.018822        1.085858   \n",
       "4         0.199834  ...         3.422701     -0.427866        0.638106   \n",
       "\n",
       "   quote number  status length  number urls   baddies  mentions  time_diff  \\\n",
       "0     -0.369992      -1.036127    -0.796091  0.047430  0.356495  -1.888186   \n",
       "1     -1.545285      -0.188945    -1.875745 -0.626192 -1.972207   0.160925   \n",
       "2     -1.545285       0.637869     0.884530 -0.096918  0.348954   0.698841   \n",
       "3     -0.662393      -0.701835     0.088472 -0.626192 -1.254997  -1.576801   \n",
       "4     -1.545285       1.370832     0.655433  0.955922 -1.914894   0.803553   \n",
       "\n",
       "   time_diff_median  \n",
       "0         -1.299249  \n",
       "1         -1.512603  \n",
       "2          0.122176  \n",
       "3         -1.311031  \n",
       "4          1.472247  \n",
       "\n",
       "[5 rows x 205 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next load the graph\n",
    "\n",
    "Now that we have the node features prepared for machine learning, let us load the retweet graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "g_nx = nx.read_edgelist(path=os.path.expanduser(os.path.join(data_dir,\n",
    "                                                             \"users.edges\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100386, 2194979)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_nx.number_of_nodes(), g_nx.number_of_edges()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph has just over 100k nodes and approximately 2.2m edges.\n",
    "\n",
    "We aim to train a graph neural network model that will predict the \"hate\"attribute on the nodes.\n",
    "\n",
    "For computation convenience, we have mapped the target labels **normal**, **hateful**, and **other** to the numeric values **0**, **1**, and **2** respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0, 1, 2}\n"
     ]
    }
   ],
   "source": [
    "print(set(node_data[\"hate\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For machine learning we want to take a subset of the nodes for training, and use the rest for validation and testing. We'll use scikit-learn again to split our data into training and test sets.\n",
    "\n",
    "The total number of annotated nodes is very small when compared to the total number of nodes in the graph. We are only going to use 15% of the annotated nodes for training and the remaining 85% of nodes for testing.\n",
    "\n",
    "First, we are going to select the subset of nodes that are annotated as hateful or normal. These will be the nodes that have 'hate' values that are either 0 or 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# choose the nodes annotated with normal or hateful classes\n",
    "annotated_users = node_data[node_data['hate']!=2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hate</th>\n",
       "      <th>statuses_count</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>followees_count</th>\n",
       "      <th>favorites_count</th>\n",
       "      <th>listed_count</th>\n",
       "      <th>negotiate_empath</th>\n",
       "      <th>vehicle_empath</th>\n",
       "      <th>science_empath</th>\n",
       "      <th>timidity_empath</th>\n",
       "      <th>...</th>\n",
       "      <th>number hashtags</th>\n",
       "      <th>tweet number</th>\n",
       "      <th>retweet number</th>\n",
       "      <th>quote number</th>\n",
       "      <th>status length</th>\n",
       "      <th>number urls</th>\n",
       "      <th>baddies</th>\n",
       "      <th>mentions</th>\n",
       "      <th>time_diff</th>\n",
       "      <th>time_diff_median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.541150</td>\n",
       "      <td>0.046773</td>\n",
       "      <td>1.104767</td>\n",
       "      <td>1.869391</td>\n",
       "      <td>0.017835</td>\n",
       "      <td>-1.752256</td>\n",
       "      <td>0.164900</td>\n",
       "      <td>0.181173</td>\n",
       "      <td>0.875069</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.632488</td>\n",
       "      <td>-0.049013</td>\n",
       "      <td>0.321929</td>\n",
       "      <td>-0.369992</td>\n",
       "      <td>-1.036127</td>\n",
       "      <td>-0.796091</td>\n",
       "      <td>0.047430</td>\n",
       "      <td>0.356495</td>\n",
       "      <td>-1.888186</td>\n",
       "      <td>-1.299249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>1.610235</td>\n",
       "      <td>-0.311474</td>\n",
       "      <td>0.224841</td>\n",
       "      <td>-1.350517</td>\n",
       "      <td>-0.883454</td>\n",
       "      <td>-1.178383</td>\n",
       "      <td>-1.035463</td>\n",
       "      <td>-0.172829</td>\n",
       "      <td>-0.253731</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.291040</td>\n",
       "      <td>1.239586</td>\n",
       "      <td>-1.215177</td>\n",
       "      <td>1.557857</td>\n",
       "      <td>-1.689390</td>\n",
       "      <td>-0.914795</td>\n",
       "      <td>-0.023220</td>\n",
       "      <td>-0.029203</td>\n",
       "      <td>-0.850158</td>\n",
       "      <td>-1.073416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0.676296</td>\n",
       "      <td>0.907563</td>\n",
       "      <td>-0.583110</td>\n",
       "      <td>-0.473819</td>\n",
       "      <td>1.029022</td>\n",
       "      <td>0.133058</td>\n",
       "      <td>-0.272510</td>\n",
       "      <td>0.154618</td>\n",
       "      <td>1.168792</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.448834</td>\n",
       "      <td>1.060920</td>\n",
       "      <td>-0.907346</td>\n",
       "      <td>0.969577</td>\n",
       "      <td>0.747312</td>\n",
       "      <td>-0.432903</td>\n",
       "      <td>0.529664</td>\n",
       "      <td>0.791479</td>\n",
       "      <td>-0.834524</td>\n",
       "      <td>-0.737971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.564326</td>\n",
       "      <td>-0.167266</td>\n",
       "      <td>0.174262</td>\n",
       "      <td>-0.364344</td>\n",
       "      <td>-0.689414</td>\n",
       "      <td>0.560514</td>\n",
       "      <td>-0.332782</td>\n",
       "      <td>0.226878</td>\n",
       "      <td>1.359779</td>\n",
       "      <td>...</td>\n",
       "      <td>0.483518</td>\n",
       "      <td>-1.269935</td>\n",
       "      <td>1.264966</td>\n",
       "      <td>-0.254387</td>\n",
       "      <td>0.863865</td>\n",
       "      <td>0.543677</td>\n",
       "      <td>0.047430</td>\n",
       "      <td>0.661312</td>\n",
       "      <td>0.758658</td>\n",
       "      <td>1.137312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0</td>\n",
       "      <td>1.036340</td>\n",
       "      <td>-0.137409</td>\n",
       "      <td>-0.777300</td>\n",
       "      <td>1.135700</td>\n",
       "      <td>-0.145647</td>\n",
       "      <td>-0.882219</td>\n",
       "      <td>-1.107841</td>\n",
       "      <td>1.624682</td>\n",
       "      <td>0.028468</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.476405</td>\n",
       "      <td>0.374573</td>\n",
       "      <td>-0.074243</td>\n",
       "      <td>0.355409</td>\n",
       "      <td>-0.302047</td>\n",
       "      <td>-0.340005</td>\n",
       "      <td>-0.524781</td>\n",
       "      <td>0.271756</td>\n",
       "      <td>-0.559000</td>\n",
       "      <td>-0.665185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 205 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    hate  statuses_count  followers_count  followees_count  favorites_count  \\\n",
       "0      0        1.541150         0.046773         1.104767         1.869391   \n",
       "22     0        1.610235        -0.311474         0.224841        -1.350517   \n",
       "29     0        0.676296         0.907563        -0.583110        -0.473819   \n",
       "44     0       -0.564326        -0.167266         0.174262        -0.364344   \n",
       "85     0        1.036340        -0.137409        -0.777300         1.135700   \n",
       "\n",
       "    listed_count  negotiate_empath  vehicle_empath  science_empath  \\\n",
       "0       0.017835         -1.752256        0.164900        0.181173   \n",
       "22     -0.883454         -1.178383       -1.035463       -0.172829   \n",
       "29      1.029022          0.133058       -0.272510        0.154618   \n",
       "44     -0.689414          0.560514       -0.332782        0.226878   \n",
       "85     -0.145647         -0.882219       -1.107841        1.624682   \n",
       "\n",
       "    timidity_empath  ...  number hashtags  tweet number  retweet number  \\\n",
       "0          0.875069  ...        -0.632488     -0.049013        0.321929   \n",
       "22        -0.253731  ...        -1.291040      1.239586       -1.215177   \n",
       "29         1.168792  ...        -0.448834      1.060920       -0.907346   \n",
       "44         1.359779  ...         0.483518     -1.269935        1.264966   \n",
       "85         0.028468  ...        -0.476405      0.374573       -0.074243   \n",
       "\n",
       "    quote number  status length  number urls   baddies  mentions  time_diff  \\\n",
       "0      -0.369992      -1.036127    -0.796091  0.047430  0.356495  -1.888186   \n",
       "22      1.557857      -1.689390    -0.914795 -0.023220 -0.029203  -0.850158   \n",
       "29      0.969577       0.747312    -0.432903  0.529664  0.791479  -0.834524   \n",
       "44     -0.254387       0.863865     0.543677  0.047430  0.661312   0.758658   \n",
       "85      0.355409      -0.302047    -0.340005 -0.524781  0.271756  -0.559000   \n",
       "\n",
       "    time_diff_median  \n",
       "0          -1.299249  \n",
       "22         -1.073416  \n",
       "29         -0.737971  \n",
       "44          1.137312  \n",
       "85         -0.665185  \n",
       "\n",
       "[5 rows x 205 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annotated_users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4971, 205)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annotated_users.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "annotated_user_features = annotated_users.drop(columns=['hate'])\n",
    "annotated_user_targets = annotated_users[['hate']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 4971 annoted nodes out of a possible, approximately, 100k nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    4427\n",
      "1     544\n",
      "Name: hate, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(annotated_user_targets.hate.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sizes and class distributions for train/test data\n",
      "Shape train_data (745, 204)\n",
      "Shape test_data (4226, 204)\n",
      "Train data number of 0s 664 and 1s 81\n",
      "Test data number of 0s 3763 and 1s 463\n"
     ]
    }
   ],
   "source": [
    "# split the data\n",
    "train_data, test_data, train_targets, test_targets = train_test_split(annotated_user_features,\n",
    "                                         annotated_user_targets,\n",
    "                                         test_size=0.85,\n",
    "                                         random_state=101)\n",
    "train_targets = train_targets.values\n",
    "test_targets = test_targets.values\n",
    "print(\"Sizes and class distributions for train/test data\")\n",
    "print(\"Shape train_data {}\".format(train_data.shape))\n",
    "print(\"Shape test_data {}\".format(test_data.shape))\n",
    "print(\"Train data number of 0s {} and 1s {}\".format(np.sum(train_targets==0), \n",
    "                                                    np.sum(train_targets==1)))\n",
    "print(\"Test data number of 0s {} and 1s {}\".format(np.sum(test_targets==0), \n",
    "                                                   np.sum(test_targets==1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((745, 1), (4226, 1))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_targets.shape, test_targets.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((745, 204), (4226, 204))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape, test_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to use 745 nodes for training and 4226 nodes for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# choosing features to assign to a graph, excluding target variable\n",
    "node_features = node_data.drop(columns=['hate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with imbalanced data\n",
    "\n",
    "Because the training data exhibit high imbalance, we introduce class weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.5609939759036144, 1: 4.598765432098766}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "class_weights = compute_class_weight('balanced', \n",
    "                                     np.unique(train_targets), \n",
    "                                     train_targets[:,0])\n",
    "train_class_weights = dict(zip(np.unique(train_targets), \n",
    "                               class_weights))\n",
    "train_class_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data is now ready for machine learning.\n",
    "\n",
    "Node features are stored in the Pandas DataFrame `node_features`.\n",
    "\n",
    "The graph in networkx format is stored in the variable `g_nx`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specify global parameters\n",
    "\n",
    "Here we specify some parameters that control the type of model we are going to use. For example, we specify the base model type, e.g., GCN, GraphSAGE, etc, as well as model-specific parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_type = 'graphsage'    # Can be either gcn, gat, or graphsage\n",
    "\n",
    "if model_type == \"graphsage\":\n",
    "    # For GraphSAGE model\n",
    "    batch_size = 50; \n",
    "    num_samples = [20, 10]\n",
    "    epochs = 30          # The number of training epochs\n",
    "elif model_type == \"gcn\":\n",
    "    # For GCN model\n",
    "    epochs = 20          # The number of training epochs\n",
    "elif model_type == \"gat\":\n",
    "    # For GAT model\n",
    "    layer_sizes = [8, 1]\n",
    "    attention_heads = 8\n",
    "    epochs = 20         # The number of training epochs    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the base graph machine learning model in Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now create a `StellarGraph` object from the `NetworkX` graph and the node features and targets. It is `StellarGraph` objects that we use in this library to perform machine learning tasks on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = sg.StellarGraph(g_nx, node_features=node_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To feed data from the graph to the Keras model we need a generator. The generators are specialized to the model and the learning task. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For training we map only the training nodes returned from our splitter and the target values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "if model_type == 'graphsage':\n",
    "    generator = GraphSAGENodeGenerator(G, batch_size, num_samples)\n",
    "    train_gen = generator.flow(train_data.index, \n",
    "                               train_targets, \n",
    "                               shuffle=True)\n",
    "elif model_type == 'gcn': \n",
    "    generator = FullBatchNodeGenerator(G, method=\"gcn\", sparse=True)\n",
    "    train_gen = generator.flow(train_data.index, \n",
    "                               train_targets, )\n",
    "elif model_type == 'gat':\n",
    "    generator = FullBatchNodeGenerator(G, method=\"gat\", sparse=True)\n",
    "    train_gen = generator.flow(train_data.index, \n",
    "                               train_targets,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we create the GNN model. We need to specify model-specific parameters based on whether we want to use GCN, GAT, or GraphSAGE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if model_type == 'graphsage':\n",
    "    base_model = GraphSAGE(\n",
    "        layer_sizes=[32, 32],\n",
    "        generator=train_gen,\n",
    "        bias=True,\n",
    "        dropout=0.5,\n",
    "    )\n",
    "    x_inp, x_out = base_model.default_model(flatten_output=True)\n",
    "    prediction = layers.Dense(units=1, activation=\"sigmoid\")(x_out)\n",
    "elif model_type == 'gcn':\n",
    "    base_model = GCN(\n",
    "        layer_sizes=[32, 16],\n",
    "        generator = generator,\n",
    "        bias=True,\n",
    "        dropout=0.5,\n",
    "        activations=[\"elu\", \"elu\"]\n",
    "    )\n",
    "    x_inp, x_out = base_model.build()\n",
    "    prediction = layers.Dense(units=1, activation=\"sigmoid\")(x_out)\n",
    "elif model_type == 'gat':\n",
    "    base_model = GAT(\n",
    "        layer_sizes=layer_sizes,\n",
    "        attn_heads=attention_heads,\n",
    "        generator=generator,\n",
    "        bias=True,\n",
    "        in_dropout=0.5,\n",
    "        attn_dropout=0.5,\n",
    "        activations=[\"elu\", \"sigmoid\"],\n",
    "        normalize=None,\n",
    "    )\n",
    "    x_inp, prediction = base_model.build()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a Keras model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the actual Keras model with the graph inputs `x_inp` provided by the `base_model` and outputs being the predictions from the softmax layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs=x_inp, outputs=prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We compile our Keras model to use the `Adam` optimiser and the binary cross entropy loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer=optimizers.Adam(lr=0.005),\n",
    "    loss=losses.binary_crossentropy,\n",
    "    metrics=[\"acc\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.engine.training.Model at 0x219e65da0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model, keeping track of its loss and accuracy on the training set, and its performance on the test set during the training. We don't use the test set during training but only for measuring the trained model's generalization performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_gen = generator.flow(test_data.index, test_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can train the model by calling the `fit_generator` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_weight = None\n",
    "if model_type == 'graphsage':\n",
    "    class_weight=train_class_weights\n",
    "history = model.fit_generator(\n",
    "    train_gen,\n",
    "    epochs=epochs,\n",
    "    validation_data=test_gen,\n",
    "    verbose=0,\n",
    "    shuffle=False,\n",
    "    class_weight=class_weight,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have trained the model, let's evaluate it on the test set.\n",
    "\n",
    "We are going to consider 4 evaluation metrics calculated on the test set: Accuracy, Area Under the ROC curve (AU-ROC), the ROC curve, and the confusion table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Set Metrics:\n",
      "\tloss: 0.3424\n",
      "\tacc: 0.8874\n"
     ]
    }
   ],
   "source": [
    "test_metrics = model.evaluate_generator(test_gen)\n",
    "print(\"\\nTest Set Metrics:\")\n",
    "for name, val in zip(model.metrics_names, test_metrics):\n",
    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AU-ROC\n",
    "\n",
    "Let's use the trained GNN model to make a prediction for each node in the graph.\n",
    "\n",
    "Then, select only the predictions for the nodes in the test set and calculate the AU-ROC as another performance metric in addition to the accuracy shown above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_nodes = node_data.index\n",
    "all_gen = generator.flow(all_nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_predictions = model.predict_generator(all_gen).squeeze()[..., np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100386, 1)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_predictions_df = pd.DataFrame(all_predictions, \n",
    "                                  index=node_data.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's extract the predictions for the test data only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_preds = all_predictions_df.loc[test_data.index, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4226, 1)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_preds.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predictions are the probability of the true class that in this case is the probability of a user being hateful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11717</th>\n",
       "      <td>0.978688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63824</th>\n",
       "      <td>0.027375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19161</th>\n",
       "      <td>0.021946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57412</th>\n",
       "      <td>0.019605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98222</th>\n",
       "      <td>0.052264</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0\n",
       "11717  0.978688\n",
       "63824  0.027375\n",
       "19161  0.021946\n",
       "57412  0.019605\n",
       "98222  0.052264"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_preds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The AUC on test set:\n",
      "\n",
      "0.8845494008100929\n"
     ]
    }
   ],
   "source": [
    "test_predictions = test_preds.values\n",
    "test_predictions_class = ((test_predictions>0.5)*1).flatten()\n",
    "test_df = pd.DataFrame({\"Predicted_score\": test_predictions.flatten(), \n",
    "                        \"Predicted_class\": test_predictions_class, \n",
    "                        \"True\": test_targets[:,0]})\n",
    "roc_auc = metrics.roc_auc_score(test_df['True'].values, \n",
    "                                test_df['Predicted_score'].values)\n",
    "print(\"The AUC on test set:\\n\")\n",
    "print(roc_auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Confusion table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted_class</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3414</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>136</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted_class     0    1\n",
       "True                      \n",
       "0                3414  349\n",
       "1                 136  327"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(test_df['True'], test_df['Predicted_class'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, thresholds = metrics.roc_curve(test_df['True'], test_df['Predicted_score'], pos_label=1)\n",
    "plt.figure(figsize=(12,6,))\n",
    "\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr, color='darkblue',\n",
    "         lw=lw, label='GNN ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate', fontsize=18)\n",
    "plt.ylabel('True Positive Rate', fontsize=18)\n",
    "plt.title('Receiver operating characteristic curve', fontsize=18)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation of node embeddings\n",
    "\n",
    "Evaluate node embeddings as activations of the output of one of the graph convolutional or aggregation layers in the Keras model, and visualise them, coloring nodes by their subject label.\n",
    "\n",
    "You can find the index of the layer of interest by calling `model.layers`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, create a Keras model for calculating the embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<keras.engine.input_layer.InputLayer at 0x2016edda0>,\n",
       " <keras.engine.input_layer.InputLayer at 0x2016edf98>,\n",
       " <keras.engine.input_layer.InputLayer at 0x2016ed940>,\n",
       " <keras.layers.core.Reshape at 0x2016edc88>,\n",
       " <keras.layers.core.Reshape at 0x2016edf28>,\n",
       " <keras.layers.core.Dropout at 0x2016edd30>,\n",
       " <keras.layers.core.Dropout at 0x2016ed908>,\n",
       " <keras.layers.core.Dropout at 0x2016edc50>,\n",
       " <keras.layers.core.Dropout at 0x20b369828>,\n",
       " <stellargraph.layer.graphsage.MeanAggregator at 0x12b7d48d0>,\n",
       " <keras.layers.core.Reshape at 0x2016edbe0>,\n",
       " <keras.layers.core.Dropout at 0x21bc2b6a0>,\n",
       " <keras.layers.core.Dropout at 0x2016edc18>,\n",
       " <stellargraph.layer.graphsage.MeanAggregator at 0x2016ed7b8>,\n",
       " <keras.layers.core.Lambda at 0x2016ed6d8>,\n",
       " <keras.layers.core.Reshape at 0x21bc2b588>,\n",
       " <keras.layers.core.Dense at 0x21bc2bf28>]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "if model_type == 'graphsage':\n",
    "    # For GraphSAGE, we are going to use the output activations \n",
    "    # of the second GraphSAGE layer as the node embeddings\n",
    "    # x_inp, prediction\n",
    "    emb_model = Model(inputs=x_inp, outputs=model.layers[-4].output)\n",
    "    emb = emb_model.predict_generator(generator=all_gen, )\n",
    "elif model_type == 'gcn':\n",
    "    # For GCN, we are going to use the output activations of \n",
    "    # the second GCN layer as the node embeddings\n",
    "    emb_model = Model(inputs=x_inp, outputs=model.layers[6].output)\n",
    "    emb = emb_model.predict_generator(generator=all_gen)\n",
    "elif model_type == 'gat':\n",
    "    # For GAT, we are going to use the output activations of the \n",
    "    # first Graph Attention layer as the node embeddings\n",
    "    emb_model = Model(inputs=x_inp, outputs=model.layers[6].output)\n",
    "    emb = emb_model.predict_generator(generator=all_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100386, 1, 32)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "emb = emb.squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "if model_type == \"graphsage\":\n",
    "    emb_all_df = pd.DataFrame(emb, index=node_data.index)\n",
    "elif model_type == \"gcn\" or model_type == \"gat\":\n",
    "    emb_all_df = pd.DataFrame(emb, index=G.nodes())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select the embeddings for the test set. We are only going to visualise the test set embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "emb_test = emb_all_df.loc[test_data.index, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their subject label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = emb_test\n",
    "y = test_targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4226, 32)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = TSNE # or use PCA \n",
    "\n",
    "trans = transform(n_components=2)\n",
    "emb_transformed = pd.DataFrame(trans.fit_transform(X), index=test_data.index)\n",
    "emb_transformed['label'] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = 0.7\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14,8,))\n",
    "ax.scatter(emb_transformed[0], emb_transformed[1], c=emb_transformed['label'].astype(\"category\"), \n",
    "            cmap=\"jet\", alpha=alpha)\n",
    "ax.set(xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
    "plt.title('{} visualization of embeddings for tweeter dataset'.format(transform.__name__), fontsize=24)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The node embeddings shown above indicate that the majority of hateful users tend to cluster together. However, some normal users are also in the same neighbourhood and these will be difficult to distinguish from hateful ones. Similarly, there is a small number of hateful users dispersed among normal users and these will also be difficult classify correctly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions using Logistic Regression\n",
    "\n",
    "Finally, we train a Logistic Regression model on the same train and test data but this time ignoring the graph structure and focusing entirely on the node features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variables `train_data`, `test_data`, `train_targets`, and `test_targets`, hold the data we need to train the Logistic Regression classifier. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegressionCV(cv=5, \n",
    "                          class_weight=class_weight, \n",
    "                          max_iter=10000)  # Let's use the default parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=10,\n",
       "           class_weight={0: 0.5609939759036144, 1: 4.598765432098766},\n",
       "           cv=5, dual=False, fit_intercept=True, intercept_scaling=1.0,\n",
       "           max_iter=10000, multi_class='warn', n_jobs=None, penalty='l2',\n",
       "           random_state=None, refit=True, scoring=None, solver='lbfgs',\n",
       "           tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(train_data, train_targets.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now use the trained model to predict the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_preds_lr = lr.predict_proba(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4226, 2)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_preds_lr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8589682915286323"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(test_data, test_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate AU-ROC metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The AUC on test set:\n",
      "\n",
      "0.8090002175324247\n"
     ]
    }
   ],
   "source": [
    "test_predictions_class_lr = ((test_preds_lr[:, 1]>0.5)*1).flatten()\n",
    "test_df_lr = pd.DataFrame({\"Predicted_score\": test_preds_lr[:, 1].flatten(), \n",
    "                        \"Predicted_class\": test_predictions_class_lr, \n",
    "                        \"True\": test_targets[:,0]})\n",
    "roc_auc_lr = metrics.roc_auc_score(test_df_lr['True'].values, test_df_lr['Predicted_score'].values)\n",
    "print(\"The AUC on test set:\\n\")\n",
    "print(roc_auc_lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The confusion table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Predicted_class</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3402</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>235</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted_class     0    1\n",
       "True                      \n",
       "0                3402  361\n",
       "1                 235  228"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(test_df_lr['True'], test_df_lr['Predicted_class'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr_lr, tpr_lr, thresholds_lr = metrics.roc_curve(test_df_lr['True'], test_df_lr['Predicted_score'], pos_label=1)\n",
    "plt.figure(figsize=(12,6,))\n",
    "lw = 2\n",
    "plt.plot(fpr_lr, tpr_lr, color='darkorange',\n",
    "         lw=lw, label='LR ROC curve (area = %0.2f)' % roc_auc_lr)\n",
    "plt.plot(fpr, tpr, color='darkblue',\n",
    "         lw=lw, label='GNN ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate', fontsize=18)\n",
    "plt.ylabel('True Positive Rate', fontsize=18)\n",
    "plt.title('Receiver operating characteristic curve', fontsize=18)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a closer look at the True Positive Rate for the GNN and Logistic Regression models at 2% False Positive Rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At 2% FPR, GNN TPR=0.378, LR TPR=0.253\n"
     ]
    }
   ],
   "source": [
    "print(\"At 2% FPR, GNN TPR={:.3f}, LR TPR={:.3f}\".format(np.interp(0.02, fpr, tpr), \n",
    "                                                        np.interp(0.02, fpr_lr, tpr_lr)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Comparison between LR and GNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Note:** This comparison is valid when comparing GraphSAGE with Logistic Regression using a specific split of the data. Using a different GNN algorithm will very likely produce different numerical results, although, the conclusion below still generally stands.\n",
    "\n",
    "Comparing the ROC curves between the two machine leanring methods, we see that adding the relatioship information in our machine learning model via the training of a GNN, improves overall performance.\n",
    "\n",
    "When classifying a user as hateful, it is important to minimise the number of false positives that is the number of normal users that are incorrectly classified as hateful. At the same time, we would like to classify as many hateful users as possible. We can achieve both of these goals by setting decision thresholds guided by the ROC curve. \n",
    "\n",
    "If we are willing to tolerate 2% false positive rate, then the GNN model achieves a true positive rate of >10% higher than the logistic regression model. That is we can correctly identify more hateful users for the same, low number, of misclassified users. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
